Melanoma is the most prevalent skin cancer and sometimes it is very difficult to diagnose. Noninvasive dermatoscopy is used to diagnose type of cancer. Since proposed method is based on eye-deduction, diagnosis of melanoma in early stage is difficult for dermatologist. A new algorithm is presented to classify dermoscopic images into malignant and benign. Initially the images were segmented using active counter model and two features such as texture and colorful components were extracted. Texture-based features were first in this area used to diagnose disease and its results indicated high-efficacy. In the international skin imaging collaboration dataset we achieve accuracy of 97% by support vector machine classifier.

The genetic traits in the Electroencephalogram (EEG), has made it a possible characteristic for the continuous biometric human verification. It is important for a biometric characteristic to have an acceptable performance on large populations besides satisfying the main requirements of a biometric system such as uniqueness, universality, acceptability, permanence, collectability, circumvention and measurability. In the present work we have proposed an EEG based human authentication system on a large dataset containing 104 healthy subjects; an issue which has not been focused in the literature. The method has been examined in two situations, closed and opened eyes. By extracting Autoregressive (AR) coefficients as the feature set, selecting the features using a statistical based method, doing a two-step feature normalization and selecting reference sweeps, the proposed system achieved to 97.43±0.00% accuracy using only 10% of the data in the training step.